Interim Efficient Allocations under Uncertainty

This paper considers an exchange economy under uncertainty with asymmetric information. Uncertainty is represented by multiple priors and posteriors of agents who have either Bewley's incomplete preferences or Gilboa-Schmeidler's maximin expected utility preferences. The main results characterize interim efficient allocations under uncertainty; that is, they provide conditions on the sets of posteriors, thus implicitly on the way how agents update the sets of priors, for non-existence of a trade which makes all agents better off at any realization of private information. For agents with the incomplete preferences, the condition is necessary and sufficient, but for agents with the maximin expected utility preferences, the condition is sufficient only. A couple of necessary conditions for the latter case are provided.multiple priors; interim efficiency; no trade; dynamic consistency; rectangular prior set

Half empty, half full and the possibility of agreeing to disagree

Aumann (1976) derives his famous we cannot agree to disagree result under the assumption of rational Bayesian learning. Motivated by psychological evidence against this assumption, we develop formal models of optimistically, resp. pessimistically, biased Bayesian learning within the framework of Choquet expected utility theory. As a key feature of our approach the posterior subjective beliefs do, in general, not converge to "true" probabilities. Moreover, the posteriors of different people can converge to different beliefs even if these people receive the same information. As our main contribution we show that people may well agree to disagree if their Bayesian learning is psychologically biased in our sense. Remarkably, this finding holds regardless of whether people with identical priors apply the same psychologically biased Bayesian learning rule or not. A simple example about the possibility of ex-post trading in a financial asset illustrates our formal findings. Finally, our analysis settles a discussion in the no-trade literature (cf. Dow, Madrigal, and Werlang 1990, Halevy 1998) in that it clarifies that ex-post trade between agents with common priors and identical learning rules is only possible under asymmetric information.Common Knowledge, No-Trade Results, Rational Bayesian Learning, Bounded Rationality, Choquet Expected Utility Theory, Bayesian Updating, Dynamic Inconsistency

Trade with Heterogeneous Multiple Priors

This paper presents a general framework to understand the possibility of a purely speculative trade under asymmetric information, where the decision making rule of each trader conforms to the multiple priors model (Gibloa and Schmeidler, 1989): the agents are interested in the minimum of the conditional expected value of trade where the minimum is taken over the set of posteriors. In this framework, we derive a necessary and sufficient condition on the sets of posteriors, thus implicitly on the updating rules adopted by the agents, for non-existence of trade such that it is always common knowledge that every agent expects a positive gain.no trade; dynamic consistency; interim efficiency; rectangularity

An Agent-Based Simulation API for Speculative PDES Runtime Environments

Agent-Based Modeling and Simulation (ABMS) is an effective paradigm to model systems exhibiting complex interactions, also with the goal of studying the emergent behavior of these systems. While ABMS has been effectively used in many disciplines, many successful models are still run only sequentially. Relying on simple and easy-to-use languages such as NetLogo limits the possibility to benefit from more effective runtime paradigms, such as speculative Parallel Discrete Event Simulation (PDES). In this paper, we discuss a semantically-rich API allowing to implement Agent-Based Models in a simple and effective way. We also describe the critical points which should be taken into account to implement this API in a speculative PDES environment, to scale up simulations on distributed massively-parallel clusters. We present an experimental assessment showing how our proposal allows to implement complicated interactions with a reduced complexity, while delivering a non-negligible performance increase

On attitude polarization under Bayesian learning with non-additive beliefs

Ample psychological evidence suggests that people’s learning behavior is often prone to a "myside bias" or "irrational belief persistence" in contrast to learning behavior exclusively based on objective data. In the context of Bayesian learning such a bias may result in diverging posterior beliefs and attitude polarization even if agents receive identical information. Such patterns cannot be explained by the standard model of rational Bayesian learning that implies convergent beliefs. As our key contribution, we therefore develop formal models of Bayesian learning with psychological bias as alternatives to rational Bayesian learning. We derive conditions under which beliefs may diverge in the learning process despite the fact that all agents observe the same - arbitrarily large - sample, which is drawn from an "objective" i.i.d. process. Furthermore, one of our learning scenarios results in attitude polarization even in the case of common priors. Key to our approach is the assumption of ambiguous beliefs that are formalized as non-additive probability measures arising in Choquet expected utility theory. As a specific feature of our approach, our models of Bayesian learning with psychological bias reduce to rational Bayesian learning in the absence of ambiguity.Non-additive Probability Measures, Choquet Expected Utility Theory, Bayesian Learning, Bounded Rationality

Speculative Growth

We propose a framework for understanding recurrent historical episodes of vigorous economic expansion accompanied by extreme asset valuations, as exhibited by Japan in the 1980's and the U.S. in the 1990's. We interpret this phenomenon as a high-valuation equilibrium with a low effective cost of capital based on optimism about the future availability of funds for investment. The key to the sustainability of such equilibrium is feedback from increased growth to an increase in the supply of funding. We show that such feedback arises naturally when the expansion is concentrated in a new economy' sector and when it is supported by sustained financial surpluses-both of which would constitute an integral part, as cause and consequence, of a speculative growth' equilibrium. The high-valuation equilibrium we analyze may take the form of a stock market bubble. In contrast to classic bubbles on non-productive assets, the bubbles in our model encourage real investments, boost long run savings, and may appear in dynamically efficient economies.

Speculative Growth: Hints from the US Economy

We propose a framework for understanding recurrent historical episodes of vigorous economic expansion accompanied by extreme asset valuations, as exhibited by the U.S. in the 1990s. We interpret this phenomenon as a high-valuation equilibrium with a low effective cost of capital based on optimism about the future availability of funds for investment. The key to the sustainability of such an equilibrium is feedback from increased growth to an increase in the supply of effective funding. We show that such feedback arises naturally when an expansion comes with technological progress in the capital producing sector, when fiscal rules generate sustained fiscal surpluses, when the rest of the world has lower expansion potential, and when financial constraints are relaxed by the expansion itself. Arguably, these ingredients were all simultaneously present in the U.S. during the 1990s. We also show that such expansions can be welfare improving but they can crash. The latter is more likely if bubbles develop along the expansionary path. These (rational) bubbles can emerge even when the interest rate exceeds the rate of growth of the economy.

Financial Crash, Commody Prices, and Global Inbalances

macroeconomics, financial crash, markets, commodities, world

Consumer optimism and price discrimination

In many principal-agent environments, the two parties hold different prior beliefs regarding the agent's future preferences. These differences may be due to inherent biases such as over-optimism or over-pessimism. We analyze the principal's optimal contract design under the assumption that the agent's prior is private information. In order to screen the agent's prior, the principal devises a menu of contingent contracts, some of which are 'speculative' as they involve betting on the agent's future action. We characterize the optimal menu and show that the characterization enables us to interpret real-life contract design in a variety of economic contexts